Robust ground moving target detection for airborne radar using a novel feature-based machine learning approach |
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| Institution: | 1. Florida International University, Miami, FL, USA;2. Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA;1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;2. The Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, and School of Mathematics–Physics and Finance, Anhui Polytechnic University, Wuhu, 241000, China;3. Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK;4. Department of Statistics, College of Science, Donghua University, Shanghai 201620, China;1. Department of Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200444, China;1. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China;2. School of Mathematics, Southeast University, Nanjing 210096, China;3. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;4. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, P.O. BOX 293, MeiLong Road NO. 130, Shanghai 200237, China;2. Department of Electrical Engineering and Automation, Shaoxing University, 508 Huancheng West Road, Shaoxing, Zhejiang 312000, China |
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| Abstract: | A novel ground-moving target detection method is introduced using a distinguishing target, and clutter feature for airborne radar. The clutter proximity feature is extracted based on the Euclidean distance between a signal pixel and the expected clutter ridge in the angle-Doppler domain. Subsequently, target and clutter pixels are classified based on the extracted features for target detection without actually removing clutters or clutter estimation. The proposed technique is especially suitable for effective airborne radar target detection in the unknown ground clutter. The experimental results have validated the effectiveness of the new approach, which enables ground moving target detection in inhomogeneous clutter. |
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